Responsys to Google Data Studio

This page provides you with instructions on how to extract data from Responsys and analyze it in Google Data Studio. (If the mechanics of extracting data from Responsys seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Responsys?

Oracle Responsys, a component of Oracle Marketing Cloud, lets organizations manage and orchestrate marketing campaigns and interactions with customers across email, mobile, social, display, and the web. Responsys provides cross-channel orchestration of customer touchpoints using the medium(s) customers prefer.

What is Google Data Studio?

Google Data Studio is a simple dashboard and reporting tool. It's free and easy to use, but it lacks the sophisticated features of higher-end reporting software. Many of the connectors it supports are for Google products, but third parties have written partner connectors to a wide variety of data sources. Its drag-and-drop report editor lets users create about 15 types of charts.

Getting data out of Responsys

Responsys has a REST API that you can use to get at information stored in the platform. For example, to retrieve an email or push campaign schedule, you would call GET /rest/api/v1.3/campaigns/{campaignName}/schedule/{scheduleId}.

Sample Responsys data

Here's an example of the kind of response you might see with a query like the one above.

{
    "id": 1491,
    "scheduleType": "ONCE",
    "scheduledTime": "2019-01-25 06:00 AM",
    "launchOptions": {
        "proofLaunch": true,
        "proofLaunchEmail": "someemail@a.com",
        "proofLaunchType": "LAUNCH_TO_ADDRESS",
        "recipientLimit": 3,
        "samplingNthSelection": 1,
        "samplingNthOffset": 1,
        "samplingNthInterval": 1,
        "progressEmailAddresses": [
            "email1@a.com",
            "email2@a.com"
        ],
        "progressChunk": "CHUNK_10K",
        "links": [
            {
                "rel": "self",
                "href": "/rest/api/v1.3/campaigns/test/schedule/1491",
                "method": "POST"
            },
            {
                "rel": "createSchedule",
                "href": "/rest/api/v1.3/campaigns/test/schedule",
                "method": "GET"
            },
            {
                "rel": "updateSchedule",
                "href": "rest/api/v1.3/campaigns/test/schedule/1491",
                "method": "PUT"
            },
            {
                "rel": "deleteSchedule",
                "href": "rest/api/v1.3/campaigns/test/schedule/1491",
                "method": "DELETE"
            }
        ]
    }
}

Preparing Responsys data

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Responsys's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Loading data into Google Data Studio

Google Data Studio uses what it calls "connectors" to gain access to data. Data Studio comes bundled with 17 connectors, mostly to pull in data from other Google products. It also supports connectors to MySQL and PostgreSQL databases, and offers 200 connectors to other data sources built and supported by partners.

Using data in Google Data Studio

Google Data Studio provides a graphical canvas onto which users drag and drop datasets. Users can set dimensions and metrics, specify sorting and filtering, and tailor the way reports and charts are displayed.

Keeping Responsys data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Responsys lacks key fields that a script could use to bookmark its progression as it looks for updated data. However, you can create .csv or .txt files as part of a Responsys Connect data export job and use a date/time prefix or suffix in the file names. You could then set up your script as a cron job or continuous loop to get new data as it's exported from Responsys.

From Responsys to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Responsys data in Google Data Studio is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Responsys to Redshift, Responsys to BigQuery, Responsys to Azure Synapse Analytics, Responsys to PostgreSQL, Responsys to Panoply, and Responsys to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Responsys with Google Data Studio. With just a few clicks, Stitch starts extracting your Responsys data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Google Data Studio.